* The focus of this master's thesis is the investigation of meta learning techniques for fast model adaptation in low-data scenarios. This is especially relevant for reducing end of line commission times for high-mix low-volume systems. * You will support us in the field of data-driven modeling and identification of nonlinear dynamical systems. * You will familiarise yourself with state-of-the-art approaches in transfer learning and meta learning for system identification through a structured literature review. * Under the guidance of our researchers, you will re-implement and evaluate selected reference methods from current research (e.g. in Python or MATLAB) to build a solid methodological foundation.

* With the support of our team, you will design and develop a simulation-based validation environment to assess the performance of the implemented methods. * You will analyse and compare the adaptability and efficiency of different approaches on nonlinear system identification tasks. * Optionally, you will apply and validate your methods on a real-world valve test bench to demonstrate practical applicability. * You may publish your results in a scientific journal and present them at a conference.

Your qualifications as an Ingenious Partner:

* Ongoing master's studies in the field of electronics, technical mathematics, technical Informatics, data science or a comparable technical field. * Enjoyment of application-oriented questions of industry * Good knowledge in Python or MATLAB * Good knowledge of machine learning * High level of commitment and team spirit * English or German skills commensurate with the role

• The thesis can be completed in either language

What to expect:

* Duration of the master's thesis project: 6 months * Start date: as soon as possible, with some flexibility depending on your availability * EUR 616,44,-

• gross per month for 12 hours/week based on the collective agreement. There will be additional company benefits.

* A supportive research environment with extensive experience in supervising and guiding master's theses * Insights into interdisciplinary research at the intersection of machine learning, control engineering, and system identification * Training in scientific work and close collaboration with experts from mathematics, informatics and engineering



At AIT diversity and inclusion are of great importance. This is why we strive to inspire women to join our teams in the field of technology. We welcome applications from women, who will be given preference in case of equal qualifications after taking into account all relevant facts and circumstances of all applications.



Please submit your application documents including your CV, cover letter, relevant certificates (transcript of records) as well as a proof of identity, online.

Link: https://jobs.ait.ac.at/Job/265806